Vacuole Segmentation and Quantification in Liver Images of Wistar Rat
Autor: | Nitin Singhal, Avinash Lokhande, Sanket Deshmukh, Ratul Wasnik |
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Rok vydání: | 2020 |
Předmět: |
Pathology
medicine.medical_specialty Bile duct 0206 medical engineering Rat model Fatty liver 02 engineering and technology Vacuole Biology medicine.disease Sensitivity and Specificity 020601 biomedical engineering Rats Liver disease medicine.anatomical_structure Liver Vacuoles 0202 electrical engineering electronic engineering information engineering medicine Animals 020201 artificial intelligence & image processing Segmentation Neural Networks Computer Rats Wistar High potential |
Zdroj: | EMBC |
Popis: | Accurate detection of macro and microvesicles in rat models of fatty liver disease is crucial in evaluating the progression of liver disease and identifying potential hepatotoxic findings during drug development. In this paper, we present a deep-learning-based framework for the segmentation of vacuoles in liver images of Wistar rat and study the correlation of automated quantification with expert pathologist's manual evaluation. To address the issue of misclassification of lumina (vascular and bile duct) as large vacuoles, we propose a selective tiling technique to generate tiles that include complete lumina and large vacuoles. A binary encoder-decoder convolution neural network is trained to detect individual vacuoles. We report a sensitivity of 85% and specificity of 98%. Furthermore, the diameter and roundness of the segmented vacuoles are estimated with an error of less than 8%, which supports the high potential of our method in drug development process. |
Databáze: | OpenAIRE |
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